Genetic Programming involves the evolution of computer programs, which are usually represented by trees composed by functions and terminals. In order to assign fitness, one must evaluate the programs, which is the most time demanding step of GP. In nowadays standard approaches, the evaluation involves an interpretation step. To avoid this step, which significantly slows the algorithm, some researchers evolve, directly, machine code programs. An alternative approach is to build a Genome Compiler, i.e. a system that transforms the individuals trees in machine-code programs and executes this code. Both techniques can bring huge speed improvements. However, these approaches have some shortcomings. In this paper we present GenCo: a research project whose main goal is development of a Genetic Programming Genome Compiler system, that overcomes some of the drawbacks of current approaches, enabling high speed improvements in a wider range of domains. We will also present experimental results in a programmatic compression task, in which GenCo was, on average, 80 times faster than a standard C based GP system.

this articles accept the latter view, and argue that, on such a view, it is inconceivable to research intelligence (natural or artificial) without studying creativity

Genetic Programming involves the evolution of computer programs, which are usually represented by trees composed by functions and terminals. In order to assign fitness, one must evaluate the programs, which is the most time demanding step of GP. In nowadays standard approaches, the evaluation involves an interpretation step. To avoid this step, which significantly slows the algorithm, some researchers evolve, directly, machine code programs. An alternative approach is to build a Genome Compiler, i.e. a system that transforms the individual's trees in machine-code programs and executes this code. Both techniques can bring huge speed improvements. However, these approaches have some shortcomings. In this paper we present GenCo: a research project whose main goal is development of a Genetic Programming Genome Compiler system, that overcomes some of the drawbacks of current approaches, enabling high speed improvements in a wider range of domains. We will also present experimental results in a programmatic compression task, in which GenCo was, on average, 80 times faster than a standard C based GP system.

Abstract. According to Koestler, the notion of a bisociation denotes a connection between pieces of information from habitually separated domains or categories. In this paper, we consider a methodology to find such bisociations using a network representation of knowledge, which is called a BisoNet, because it promises to contain bisociations. In a first step, we consider how to create BisoNets from several textual databases taken from different domains using simple text-mining techniques. To achieve this, we introduce a procedure to link nodes of a BisoNet and to endow such links with weights, which is based on a new measure for comparing text frequency vectors. In a second step, we try to rediscover known bisociations, which were originally found by a human domain expert, namely indirect relations between migraine and magnesium as they are hidden in medical research articles published before 1987. We observe that these bisociations are easily rediscovered by simply following the strongest links. Future work includes extending our methods to non-textual data, improving the similarity measure, and applying more sophisticated graph mining methods. 1